Internet of Things (IoT) Training in Slovakia

This instructor-led, live training in Slovakia (online or onsite) targets advanced-level IoT developers and smart home enthusiasts looking to automate IoT processes and build innovative solutions using n8n.

By the end of this training, participants will be able to:

• Set up and configure n8n for IoT workflow automation.
• Integrate IoT devices and platforms using n8n nodes and connectors.
• Implement custom workflows to automate IoT tasks and processes.
• Use IoT protocols like MQTT and REST APIs within n8n workflows.
• Monitor, troubleshoot, and optimize IoT automation workflows.

As the next-generation standard for wireless communication, 6G is poised to revolutionize IoT ecosystems by delivering ultra-fast connectivity, advanced sensing capabilities, and seamlessly integrated AI functions.

This instructor-led live training, available both online and onsite, is designed for advanced professionals seeking to understand and capitalize on the emerging convergence of 6G technologies and IoT applications.

Upon completing this course, learners will be equipped to:

• Articulate the fundamental technical concepts underlying 6G.
• Evaluate how 6G will transform IoT device communication and architectural frameworks.
• Analyze 6G-enabled IoT use cases across various industries.
• Develop strategies for integrating 6G capabilities into current IoT solutions.

Course Format

• Concept-driven lectures paired with expert-led discussions.
• Practical exercises designed to reinforce core engineering principles.
• Guided exploration of case studies and scenario analyses.

Customization Options

• For customized training versions aligned with your organization's technology roadmap, please contact us to arrange a session.

This instructor-led, live training in Slovakia (online or onsite) is aimed at intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

By the end of this training, participants will be able to:

• Understand the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

This instructor-led, live training in Slovakia (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing IoT applications with intelligent data processing and analytics capabilities.

By the end of this training, participants will be able to:

• Understand the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in IoT systems.
• Integrate Edge AI with various IoT protocols and platforms.
• Address ethical considerations and best practices in Edge AI for IoT.

This instructor-led, live training in Slovakia (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Develop and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection using TinyML.
• Optimize TinyML models for efficient power and memory usage.

This instructor-led, live training in Slovakia (online or onsite) is designed for intermediate IT professionals and business managers who want to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across various industries.

By the end of this training, participants will be able to:

• Understand the principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
• Differentiate between edge and cloud computing architectures and deployment scenarios.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

Embedded systems are specialized computing solutions engineered to execute specific tasks within broader systems. The Internet of Things (IoT) refers to a network of physical devices equipped with sensors and software, enabling them to communicate and share data via the internet.

This instructor-led live training, available either online or on-site, targets technical professionals at the beginner level who aim to grasp and apply concepts related to embedded systems and IoT using C programming and microcontroller architectures.

Upon completion of this training, participants will be able to:

• Grasp the architecture and key components of embedded systems.
• Write and compile C code to facilitate interaction with embedded hardware.
• Utilize microcontroller peripherals, including timers and Analog-to-Digital Converters (ADCs).
• Comprehend the role of embedded systems within IoT architectures.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Hands-on implementation within a live laboratory environment.

Options for Course Customization

• For inquiries regarding customized training for this course, please reach out to us to make arrangements.

The objective of this training is to clarify the nature of 5G networks and their influence on smart technologies. The course aims to present both the advantages and disadvantages of the relationship between these technologies (5G and IoT), while highlighting the developmental directions of a network designed from the outset for the smart world.

The objective of the training is to explain what constitutes Smart Solutions (Internet of Things, AI, Blockchain, Virtual Reality, Metaverse) and what does not, while highlighting the advantages and disadvantages of these technological landscapes.

Technological advancements and the exponential growth of information are reshaping business operations across various sectors, including government. The volume of data generated by government entities and the rate of digital archiving are increasing rapidly, driven by the proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information expands in complexity, managing, processing, storing, securing, and disposing of it becomes increasingly challenging. New tools for capturing, searching, discovering, and analyzing data are enabling organizations to derive valuable insights from unstructured data. The government sector is at a critical juncture, recognizing information as a strategic asset. Agencies must now protect, leverage, and analyze both structured and unstructured information to better serve the public and meet mission objectives. As government leaders work to transform their organizations into data-driven entities, they are establishing the framework to correlate dependencies across events, people, processes, and information.

High-impact government solutions are emerging from the integration of several disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

Big Data represents a transformative industry solution that enables governments to make better decisions by acting on patterns identified through the analysis of vast volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these results requires more than just collecting large amounts of data. "Making sense of these volumes of Big Data requires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information," noted Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy in a post on the OSTP Blog.

The White House took significant steps to assist agencies in identifying these technologies by launching the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximize the potential of the Big Data explosion and the necessary analytical tools.

The challenges presented by Big Data are as formidable as its promise is encouraging. Efficiently storing data is one such challenge. With budgets always under pressure, agencies must minimize storage costs per megabyte while ensuring data remains easily accessible so users can retrieve it whenever and however they need it. Backing up massive data volumes further complicates this task.

Effective data analysis poses another major challenge. Many agencies utilize commercial tools to sift through vast amounts of data, identifying trends that enhance operational efficiency. (A recent MeriTalk study found that federal IT executives believe Big Data could help agencies save over $500 billion while fulfilling mission objectives.)

Custom-developed Big Data tools are also enabling agencies to address their analytical needs. For instance, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. This system has helped medical researchers identify links that alert doctors to aortic aneurysms before they occur. It is also used for routine tasks, such as screening resumes to match job candidates with hiring managers.

This instructor-led, live training in Slovakia (online or onsite) is aimed at product managers and developers who wish to use Edge Computing to decentralize data management for faster performance, leveraging smart devices located on the source network.

By the end of this training, participants will be able to:

• Understand the basic concepts and advantages of Edge Computing.
• Identify the use cases and examples where Edge Computing can be applied.
• Design and build Edge Computing solutions for faster data processing and reduced operational costs.

The Internet of Things (IoT) refers to a network infrastructure that wirelessly connects physical devices with software applications, enabling them to communicate and exchange data through network communications, cloud computing, and data capture. C is a general-purpose programming language recommended for IoT development due to its widespread use and benefits in low-level programming.

In this instructor-led live training, participants will learn how to develop IoT solutions using C.

By the end of the training, participants will be able to:

• Install and configure NetBeans for programming IoT systems with C
• Grasp the fundamentals of IoT architecture
• Understand the advantages of using C for IoT programming
• Build, test, deploy, and troubleshoot an IoT system using C

Audience

• Developers
• Engineers

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• To request customized training for this course, please contact us to arrange it.

The Internet of Things (IoT) represents a network infrastructure that wirelessly connects physical objects with software applications, enabling seamless communication, data exchange, and integration through network communications, cloud computing, and data capture mechanisms. Java, renowned for its "write once, run anywhere" capability, is a versatile language recommended for IoT development due to its high portability and efficiency.

This instructor-led live training will guide participants in developing IoT solutions using Java.

Upon completion of this course, participants will be able to:

• Install and configure necessary tools and frameworks, such as the Eclipse Open IoT Stack, for Java-based IoT programming
• Comprehend the core principles of IoT architecture
• Utilize the Eclipse Open IoT Stack for Java to connect and manage devices within an IoT environment
• Construct, test, and deploy IoT systems using Java

Target Audience

• Software Developers
• Engineers

Course Format

• A blend of lectures, discussions, exercises, and extensive hands-on practice

Note

• For customized training options, please reach out to us to arrange a tailored session.

Connected devices are disrupting numerous industries, with the power utility sector being no exception. Power utility companies currently face four primary challenges stemming from the proliferation of IoT:

1. Vendors are increasingly connecting machines, controllers, HMIs, and SCADA systems to the cloud, promising enhanced analytics and insights for predictive and preventative maintenance. However, stringent quarantine policies governing critical assets prevent power companies from leveraging these new IoT features offered by machine and controller vendors.
2. As the cost of solar and wind microgrids continues to decline, utility companies anticipate a drop in revenue from traditional power generation. To offset this, they must aggressively pursue new revenue streams, such as Energy Management as a Service, Energy Storage as a Service, and grid services for EV charging and peer-to-peer (P2P) energy trading among homes, microgrids, and batteries. These transactions require smart metering, smart grids, and secure exchanges facilitated by Distributed Ledger Technology (DLT) like IOTA. Additionally, utilities are exploring the provision of smart city services to municipal authorities.
3. For critical infrastructure such as dams, ICOLD (International Committee of Large Dams) mandates real-time Structural Health Monitoring (SHM) to detect impending risks of collapse in dams, rock formations, or tunnels. This allows for the early evacuation of people in affected areas.
4. Another emerging revenue area is EV charging in parking facilities, raising the question of how IoT can facilitate smart charging and parking solutions.

Over the past three years, IoT engineering has undergone significant transformations, largely driven by Microsoft, Google, and Amazon. These tech giants have invested billions to develop IoT platforms that are easier to manage and secure. IoT edge computing has gained substantial momentum as the primary method for practical IoT implementation, while 5G promises to further revolutionize the IoT business landscape. This has led to unprecedented funding in IoT research. Consequently, it is essential for practicing engineers to understand the IoT platforms developed for major players like AWS, Google, and especially Microsoft.

However, none of these platforms offer a fully comprehensive solution for scalable IoT. Deploying smart meters to millions of homes requires additional technologies for securing the meters, radio networks, IoT management systems, and other secure services. Strategy, pricing, and security in any IoT deployment must be optimized and acceptable. Given the interdisciplinary nature of this knowledge, it is challenging for any single company to assemble a team capable of meeting all requirements.

This course makes a concerted effort to educate key decision-makers, developers, and security experts on the challenges, risks, and practical approaches to deploying IoT for next-generation power utility businesses.

Furthermore, as IoT deployments scale, managing services for thousands of sensors and connections has emerged as a distinct engineering research subject. This area, formerly known as managed IoT services, is experiencing rapid growth because the challenges of scalable IoT management are greater than the implementation itself. This includes securing over-the-top firmware/software updates, managing sensor and system calibration, auto-diagnosing connection issues, identifying root causes of API failures, and tracking the health of hardware and services in distributed systems.

Course Objectives

The main objective of this course is to introduce emerging technological options, platforms, and case studies of IoT implementation in power utility companies, focusing on Smart Metering, Smart Cars, SHM (Structural Health Monitoring), Power Quality Diagnosis, and Smart Contracts. It provides a basic introduction to all IoT elements, including mechanical and electronics/sensor platforms, wireless and wireline protocols, mobile-to-electronics integration, mobile-to-enterprise integration, data analytics, and control plane applications.

1. IoT Technology Stacks: Devices, Gateways, Edge, Edge Cloud, Public Cloud, IoT databases, Web & Mobile Applications for IoT, Centralized vs. Decentralized IoT
2. IoT Ecosystem for Business: Third-party device management and risk management of the entire IoT ecosystem
3. M2M Wireless Protocols for IoT: WiFi, SigFox, LoRa, LPWAN, Zigbee/Z-Wave, Bluetooth, ANT+ - Guidelines on when and where to use each
4. Fundamentals of IoT Gateways: Risks, Management, and Ecosystem
5. Mobile/Desktop/Web Apps for registration, data acquisition, and control - Overview of available M2M data acquisition platforms for IoT (AWS IoT, Azure IoT, Google IoT)
6. Security Issues and Solutions for IoT: Review of security across all technology stacks
7. Enterprise IoT Platforms: Microsoft Azure IoT Suites, AWS IoT, Google IoT, Siemens MindSphere
8. Smart Metering Standards: Open Smart Grid Protocols (OSGP), ANSI C2.18 Protocols, NIST Standard for HAN (Home Area Network), HomePlug Powerline Alliance, Smart Meter Security Standard (IEC 62056)
9. Distributed Ledger Technology (DLT): Blockchain, HyperLedger, and DAG (Directed Acyclic Graph) for smart contracts, P2P transactions, and smart car charging
10. IoT for Critical Infrastructure: Dams, Transformers, Substations, High-Tension Wires

This instructor-led, live training in Slovakia (online or onsite) is aimed at developers and programmers who wish to install, configure, and manage the Kaa platform to build IoT applications.

By the end of this training, participants will be able to build, develop, manage, and implement IoT applications for smart devices and machines using Kaa.

In this instructor-led, live training conducted Slovakia, participants will learn how to maximize the performance of Nginx while setting up, configuring, monitoring, and troubleshooting it for handling various forms of HTTP and TCP traffic. The topics include configuring essential Nginx parameters, as well as optimizing the OS and virtual machine settings to gain maximum value from Nginx.